Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis

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Date
2019-10-01Type
- Journal Article
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Cited 54 times in
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Cited 55 times in
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Abstract
Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000368533Publication status
publishedExternal links
Journal / series
Cell ReportsVolume
Pages / Article No.
Publisher
ElsevierSubject
Gaussian Process; Random effect model; Multiplexed imagingMore
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Citations
Cited 54 times in
Web of Science
Cited 55 times in
Scopus
ETH Bibliography
yes
Altmetrics